6.4

\(\renewcommand\AA{\unicode{x212B}}\)

EnggEstimateFocussedBackground v1

../_images/EnggEstimateFocussedBackground-v1_dlg.png

EnggEstimateFocussedBackground dialog.

Summary

Performs iterative smoothing (low-pass filter) to estimate the background of a spectrum

Properties

Name Direction Type Default Description
InputWorkspace Input MatrixWorkspace Mandatory Workspace with focussed spectra
OutputWorkspace Output MatrixWorkspace Mandatory Workspace to contain the estimated background (one for each spectrum in the InputWorkspace)
NIterations Input number 50 Number of iterations of the smoothing procedure to perform. Too few iterations and the background will be enhanced in the peak regions. Too many iterations and the background will be unrealistically low and not catch the rising edge at low TOF/d-spacing (typical values are in range 20-100).
XWindow Input number 600 Extent of the convolution window in the x-axis for all spectra. A reasonable value is about 4-8 times the FWHM of a typical peak/feature to be suppressed (default is reasonable for TOF spectra). This is converted to an odd number of points using the median bin width of each spectra.
ApplyFilterSG Input boolean True Apply a Savitzky–Golay filter with a linear polynomial over the same XWindow before the iterative smoothing procedure (recommended for noisy data)

Description

Estimates the background for a spectra by employing an iterative smoothing procedure adapted from an algorithm published in [1]. In each iteration a smoothing window is convolved with the data to produce a new spectra which is compared point by point with spectra from the previous iteration, with the lowest of the two intensities retained. In this way counting statistics are taken into conisderation to some extent, but if the data are noisy then this can lead to the background being underestimated (by the approxiumate amplitude of the noise). This can be avoided by rebinning/rebunching data, however this is not always desirable. Therefore there is an option to apply a Savitzky–Golay filter with a linear polynomial over a window the same length as the smoothing window before the iterative smoothing.

Useage

Example:

from mantid.simpleapi import *

CreateSampleWorkspace(OutputWorkspace='ws', Function='Multiple Peaks', NumBanks=1, BankPixelWidth=1, XMax=20, BinWidth=0.2)
EnggEstimateFocussedBackground(InputWorkspace='ws', OutputWorkspace='ws_bg', NIterations='200', XWindow=2.5, ApplyFilterSG=False)

References

The source for how this calculation is done is

  1. Brückner, S. (2000). Estimation of the background in powder diffraction patterns through a robust smoothing procedure. Journal of Applied Crystallography, 33(3), 977-979._

Categories: AlgorithmIndex | Diffraction\Engineering